Product line pricing is an important business problem faced by retailers and other sellers of merchandise who employ dynamic pricing strategies to generate incremental revenue benefits throughout the year. Retailers, among others, have in increasing numbers begun to utilize decision support systems that leverage the large volume of detailed demand data to automate and optimize pricing recommendations. In particular, the statistical modeling of the price elasticity of items based on analyzing the effect of price changes of one product on its demand, or the demand for another product, is a well-researched area.
Among known models that implicitly capture inter-item interactions, the multinomial logit (“MNL”) model is a popular choice for discrete customer choice analysis, and has come into prominence for product line pricing in the retail industry. In this context, the market share of an item is a consequence of its relative attraction with respect to other competing items (i.e., substitutes). A retailer would like to determine an optimal category pricing strategy to set prices for items in a given category (e.g., soups, cold cereals) for the next few weeks. These items are assumed to be substitutable in that they compete for the same customer dollar. However, unlike the cross-elasticity model, the MNL model generally cannot capture the market halo effects associated with complementary items.